Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 67745

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 74308

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-25T20:52:42+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	139575
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	137708	0
dateAnnounced	0	1.00	10	10	0	302	0
gender	35931	0.74	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	135426	0.03	8	23	0	8	0
notes	74898	0.46	1	270	0	61733	1
mhlwPatientNumber	139126	0.00	1	11	0	434	0
prefecturePatientNumber	32071	0.77	5	20	0	107495	0
prefectureSourceURL	108183	0.22	5	224	0	3454	0
residence	43871	0.69	1	38	0	1429	0
sourceURL	1170	0.99	1	239	0	8760	0
relatedPatients	127714	0.08	2	259	0	7075	0
knownCluster	137048	0.02	3	88	0	234	0
detectedCityTown	111495	0.20	2	22	0	667	0
cityPrefectureNumber	111796	0.20	1	34	0	27770	2
citySourceURL	127520	0.09	9	317	0	3681	0
deceasedDate	137585	0.01	10	10	0	252	0
deceasedReportedDate	138355	0.01	10	62	0	207	0
deathSourceURL	138500	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 137707, FAL: 1868
charterFlightPassenger	139561	0	1.00	TRU: 14
cruisePassengerDisembarked	139564	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	27.94	24.7	-1	-1	20	50	100	▇▇▅▃▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	136233
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	136233	0
knownCluster	133756	0.02	3	88	0	231	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-25	2020-09-01	299
deceasedDate	135854	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	135904	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	34181	0.75	FALSE	2	M: 57161, F: 44891
patientStatus	133722	0.02	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 34266, 20: 27577, 30: 17686, 40: 14835
pcode	0	1.00	FALSE	47	13: 38647, 27: 18459, 14: 11659, 23: 9212
pref	0	1.00	FALSE	47	東京都: 38647, 大阪府: 18459, 神奈川: 11659, 愛知県: 9212
region	0	1.00	FALSE	8	関東地: 67745, 近畿地: 28990, 中部地: 14417, 九州地: 12476
広域圏	11818	0.91	FALSE	8	首都圏: 68078, 近畿圏: 28236, 中部圏: 12921, 九州圏: 8413
通俗的区分	0	1.00	FALSE	11	関東: 67745, 関西: 28236, 東海: 12284, 九州: 8413
fct_pref	0	1.00	FALSE	47	Tok: 38647, Osa: 18459, Kan: 11659, Aic: 9212

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 136233
charterFlightPassenger	136226	0	1.00	TRU: 7
cruisePassengerDisembarked	136222	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 133756, TRU: 2477

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.08）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.08）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  dplyr::mutate(hday = is.jholiday(target_date = date,
                                   holiday_source = "./Covid19/syukujitsu.csv"),
                wday = lubridate::wday(date, week_start = 1),
                wday = dplyr::if_else(wday > 5, TRUE, FALSE),
                wday = dplyr::if_else(hday | wday, 3000L,  NA_integer_)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
                      fill = "red", alpha = 0.1) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
                        colour = "dark blue", linetype = "dashed", size = 0.15) +
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.25) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              size = 2.0, data = emergency) +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
                              size = 2.5, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 202.6599 207.2495 200.1610 206.2141 196.0721 196.6964 177.8187 182.3835
##  [9] 177.7976 184.8837 184.8837 184.8837 184.8837 184.8837
## 
## $北海道地方$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 190.2841 183.7328
## 46.28571 192.1723 184.1910
## 46.42857 180.6389 170.3045
## 46.57143 185.0438 173.8370
## 46.71429 173.3731 161.3570
## 46.85714 172.5652 159.7909
## 47.00000 152.3357 138.8458
## 47.14286 153.5539 138.2924
## 47.28571 146.6191 130.1142
## 47.42857 150.9108 132.9266
## 47.57143 148.9552 129.9359
## 47.71429 147.1007 127.0997
## 47.85714 145.3331 124.3963
## 48.00000 143.6412 121.8087
## 
## $北海道地方$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 215.0357 221.5870
## 46.28571 222.3266 230.3080
## 46.42857 219.6830 230.0174
## 46.57143 227.3843 238.5912
## 46.71429 218.7712 230.7873
## 46.85714 220.8276 233.6018
## 47.00000 203.3016 216.7915
## 47.14286 211.2131 226.4746
## 47.28571 208.9760 225.4809
## 47.42857 218.8566 236.8407
## 47.57143 220.8121 239.8315
## 47.71429 222.6666 242.6677
## 47.85714 224.4342 245.3710
## 48.00000 226.1262 247.9586
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 34.13266 35.20380 32.58407 31.93657 29.95261 30.54120 30.57257 32.15839
##  [9] 32.54864 33.28225 32.69457 32.39673 31.53777 31.41131
## 
## $東北地方$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 27.20206 23.53322
## 46.28571 28.01782 24.21379
## 46.42857 25.24524 21.36030
## 46.57143 24.59383 20.70682
## 46.71429 22.56230 18.65011
## 46.85714 23.08761 19.14192
## 47.00000 22.82764 18.72772
## 47.14286 24.07868 19.80153
## 47.28571 24.02621 19.51471
## 47.42857 24.47938 19.81942
## 47.57143 23.67533 18.90083
## 47.71429 23.27189 18.44150
## 47.85714 22.31206 17.42826
## 48.00000 22.09327 17.16060
## 
## $東北地方$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 41.06325 44.73209
## 46.28571 42.38978 46.19381
## 46.42857 39.92289 43.80783
## 46.57143 39.27931 43.16633
## 46.71429 37.34291 41.25511
## 46.85714 37.99479 41.94048
## 47.00000 38.31751 42.41743
## 47.14286 40.23811 44.51525
## 47.28571 41.07107 45.58258
## 47.42857 42.08511 46.74507
## 47.57143 41.71380 46.48830
## 47.71429 41.52156 46.35196
## 47.85714 40.76348 45.64727
## 48.00000 40.72934 45.66201
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 906.1618 919.9177 951.9843 737.0846 574.9934 610.8029 823.2955 913.7092
##  [9] 905.8466 942.4997 737.9994 589.2576 662.0547 845.8556
## 
## $関東地方$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 830.4482 790.3679
## 46.28571 825.5509 775.5962
## 46.42857 847.3725 791.9943
## 46.57143 627.4908 569.4754
## 46.71429 460.8828 400.4763
## 46.85714 492.5744 429.9880
## 47.00000 701.2959 636.7133
## 47.14286 778.1798 706.4349
## 47.28571 761.1713 684.5848
## 47.42857 790.8483 710.5689
## 47.57143 581.2081 498.2079
## 47.71429 427.7504 342.2536
## 47.85714 496.2057 408.4105
## 48.00000 675.9977 586.0803
## 
## $関東地方$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286  981.8754 1021.9557
## 46.28571 1014.2844 1064.2392
## 46.42857 1056.5961 1111.9743
## 46.57143  846.6783  904.6937
## 46.71429  689.1040  749.5105
## 46.85714  729.0314  791.6178
## 47.00000  945.2950 1009.8777
## 47.14286 1049.2385 1120.9834
## 47.28571 1050.5219 1127.1084
## 47.42857 1094.1511 1174.4305
## 47.57143  894.7906  977.7908
## 47.71429  750.7648  836.2616
## 47.85714  827.9038  915.6990
## 48.00000 1015.7135 1105.6309
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 283.3456 279.7486 289.8332 224.3375 203.8268 191.3650 258.3151 256.8103
##  [9] 254.7689 260.4912 223.3276 211.6894 204.6184 242.6072
## 
## $中部地方$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 259.5606 246.96955
## 46.28571 250.1193 234.43450
## 46.42857 254.5044 235.80248
## 46.57143 184.3006 163.10642
## 46.71429 159.5357 136.08936
## 46.85714 143.2044 117.70972
## 47.00000 206.5710 179.17937
## 47.14286 195.7514 163.42870
## 47.28571 187.1967 151.42612
## 47.42857 186.6246 147.52198
## 47.57143 143.7481 101.62118
## 47.71429 126.7599  81.80085
## 47.85714 114.6614  67.04104
## 48.00000 147.8880  97.74674
## 
## $中部地方$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 307.1306 319.7216
## 46.28571 309.3779 325.0627
## 46.42857 325.1620 343.8639
## 46.57143 264.3743 285.5685
## 46.71429 248.1179 271.5642
## 46.85714 239.5256 265.0203
## 47.00000 310.0591 337.4508
## 47.14286 317.8693 350.1919
## 47.28571 322.3412 358.1118
## 47.42857 334.3579 373.4605
## 47.57143 302.9072 345.0341
## 47.71429 296.6190 341.5780
## 47.85714 294.5754 342.1958
## 48.00000 337.3264 387.4677
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 595.4209 610.1993 656.0376 659.9533 464.7721 503.0099 589.7775 658.8963
##  [9] 670.6165 713.2049 725.4077 559.6473 545.4668 629.6855
## 
## $近畿地方$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 551.6115 528.4202
## 46.28571 557.6319 529.8043
## 46.42857 600.6497 571.3292
## 46.57143 601.8818 571.1406
## 46.71429 404.1356 372.0366
## 46.85714 439.9127 406.5111
## 47.00000 524.3119 489.6565
## 47.14286 582.9544 542.7532
## 47.28571 588.4381 544.9356
## 47.42857 627.0629 581.4621
## 47.57143 635.4766 587.8699
## 47.71429 466.0803 416.5490
## 47.85714 448.4001 397.0160
## 48.00000 529.2408 476.0687
## 
## $近畿地方$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 639.2303 662.4217
## 46.28571 662.7667 690.5943
## 46.42857 711.4255 740.7460
## 46.57143 718.0248 748.7660
## 46.71429 525.4085 557.5075
## 46.85714 566.1072 599.5088
## 47.00000 655.2430 689.8984
## 47.14286 734.8381 775.0393
## 47.28571 752.7948 796.2973
## 47.42857 799.3469 844.9477
## 47.57143 815.3389 862.9456
## 47.71429 653.2143 702.7457
## 47.85714 642.5336 693.9176
## 48.00000 730.1301 783.3023
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 27.66793 28.03348 28.03348 28.03348 28.03348 28.03348 28.03348 28.03348
##  [9] 28.03348 28.03348 28.03348 28.03348 28.03348 28.03348
## 
## $中国地方$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 18.16232 13.130357
## 46.28571 17.92076 12.567417
## 46.42857 17.66107 12.170243
## 46.57143 17.40771 11.782774
## 46.71429 17.16026 11.404331
## 46.85714 16.91832 11.034310
## 47.00000 16.68153 10.672174
## 47.14286 16.44958 10.317439
## 47.28571 16.22219  9.969669
## 47.42857 15.99909  9.628469
## 47.57143 15.78005  9.293480
## 47.71429 15.56486  8.964375
## 47.85714 15.35332  8.640855
## 48.00000 15.14526  8.322643
## 
## $中国地方$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 37.17354 42.20550
## 46.28571 38.14619 43.49954
## 46.42857 38.40589 43.89671
## 46.57143 38.65924 44.28418
## 46.71429 38.90669 44.66262
## 46.85714 39.14863 45.03264
## 47.00000 39.38542 45.39478
## 47.14286 39.61737 45.74951
## 47.28571 39.84476 46.09728
## 47.42857 40.06786 46.43848
## 47.57143 40.28690 46.77347
## 47.71429 40.50209 47.10258
## 47.85714 40.71363 47.42610
## 48.00000 40.92170 47.74431
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 17.69512 18.22723 18.22723 18.22723 18.22723 18.22723 18.22723 18.22723
##  [9] 18.22723 18.22723 18.22723 18.22723 18.22723 18.22723
## 
## $四国地方$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 13.88958 11.875055
## 46.28571 13.95805 11.698083
## 46.42857 13.74234 11.368187
## 46.57143 13.53654 11.053446
## 46.71429 13.33940 10.751946
## 46.85714 13.14991 10.462143
## 47.00000 12.96724 10.182773
## 47.14286 12.79071  9.912785
## 47.28571 12.61973  9.651293
## 47.42857 12.45381  9.397542
## 47.57143 12.29252  9.150881
## 47.71429 12.13551  8.910750
## 47.85714 11.98244  8.676654
## 48.00000 11.83304  8.448160
## 
## $四国地方$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 21.50065 23.51518
## 46.28571 22.49641 24.75637
## 46.42857 22.71211 25.08627
## 46.57143 22.91791 25.40101
## 46.71429 23.11505 25.70251
## 46.85714 23.30454 25.99231
## 47.00000 23.48721 26.27168
## 47.14286 23.66375 26.54167
## 47.28571 23.83473 26.80316
## 47.42857 24.00065 27.05691
## 47.57143 24.16193 27.30357
## 47.71429 24.31895 27.54371
## 47.85714 24.47201 27.77780
## 48.00000 24.62142 28.00630
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 98.67188 99.13034 81.18715 75.02691 55.90953 54.45643 73.04138 81.40072
##  [9] 79.77850 72.97341 70.74223 58.08641 57.85450 68.69675
## 
## $九州地方$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%         95%
## 46.14286 76.4637808  64.7075211
## 46.28571 72.0470042  57.7099508
## 46.42857 50.9870678  35.0001088
## 46.57143 43.9612084  27.5160188
## 46.71429 23.4038536   6.1963885
## 46.85714 18.5161296  -0.5095177
## 47.00000 34.5939239  14.2410686
## 47.14286 37.3033429  13.9596002
## 47.28571 31.4130312   5.8099001
## 47.42857 20.8795291  -6.6973069
## 47.57143 16.3476343 -12.4471235
## 47.71429  0.9128577 -29.3529916
## 47.85714 -2.6854928 -34.7334304
## 48.00000  5.4040867 -28.1010261
## 
## $九州地方$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%      95%
## 46.14286 120.87999 132.6362
## 46.28571 126.21368 140.5507
## 46.42857 111.38724 127.3742
## 46.57143 106.09261 122.5378
## 46.71429  88.41520 105.6227
## 46.85714  90.39674 109.4224
## 47.00000 111.48884 131.8417
## 47.14286 125.49810 148.8418
## 47.28571 128.14396 153.7471
## 47.42857 125.06730 152.6441
## 47.57143 125.13682 153.9316
## 47.71429 115.25996 145.5258
## 47.85714 118.39450 150.4424
## 48.00000 131.98942 165.4945

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 202.6599 207.2495 200.1610 206.2141 196.0721 196.6964 177.8187 182.3835
##  [9] 177.7976 184.8837 184.8837 184.8837 184.8837 184.8837
## 
## $北海道$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 190.2841 183.7328
## 46.28571 192.1723 184.1910
## 46.42857 180.6389 170.3045
## 46.57143 185.0438 173.8370
## 46.71429 173.3731 161.3570
## 46.85714 172.5652 159.7909
## 47.00000 152.3357 138.8458
## 47.14286 153.5539 138.2924
## 47.28571 146.6191 130.1142
## 47.42857 150.9108 132.9266
## 47.57143 148.9552 129.9359
## 47.71429 147.1007 127.0997
## 47.85714 145.3331 124.3963
## 48.00000 143.6412 121.8087
## 
## $北海道$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 215.0357 221.5870
## 46.28571 222.3266 230.3080
## 46.42857 219.6830 230.0174
## 46.57143 227.3843 238.5912
## 46.71429 218.7712 230.7873
## 46.85714 220.8276 233.6018
## 47.00000 203.3016 216.7915
## 47.14286 211.2131 226.4746
## 47.28571 208.9760 225.4809
## 47.42857 218.8566 236.8407
## 47.57143 220.8121 239.8315
## 47.71429 222.6666 242.6677
## 47.85714 224.4342 245.3710
## 48.00000 226.1262 247.9586
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 0.8368207 0.4516365 0.6678215 0.5464875 0.6145863 0.5763658 0.5978171
##  [8] 0.5857775 0.5925348 0.5887423 0.5908708 0.5896762 0.5903467 0.5899703
## 
## $青森県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 -1.681757 -3.015011
## 46.28571 -2.502961 -4.067030
## 46.42857 -2.366719 -3.973108
## 46.57143 -2.706015 -4.427786
## 46.71429 -2.760663 -4.547413
## 46.85714 -2.959250 -4.830892
## 47.00000 -3.067948 -5.008486
## 47.14286 -3.218076 -5.231714
## 47.28571 -3.337775 -5.418356
## 47.42857 -3.467757 -5.615138
## 47.57143 -3.586000 -5.797102
## 47.71429 -3.705288 -5.978904
## 47.85714 -3.818950 -6.153091
## 48.00000 -3.931096 -6.324405
## 
## $青森県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 3.355398 4.688652
## 46.28571 3.406234 4.970303
## 46.42857 3.702362 5.308751
## 46.57143 3.798990 5.520761
## 46.71429 3.989836 5.776585
## 46.85714 4.111982 5.983624
## 47.00000 4.263582 6.204120
## 47.14286 4.389631 6.403269
## 47.28571 4.522845 6.603425
## 47.42857 4.645242 6.792623
## 47.57143 4.767741 6.978843
## 47.71429 4.884640 7.158257
## 47.85714 4.999643 7.333784
## 48.00000 5.111037 7.504345
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1]  7.739208  8.435930 14.123672 12.067502  6.928548 10.633035 12.113352
##  [8] 10.389586  7.508015 10.870153 12.259786  9.170865  9.045120 11.661903
## 
## $岩手県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286  6.335373  5.592228
## 46.28571  6.858718  6.023792
## 46.42857 12.466062 11.588576
## 46.57143 10.407455  9.528679
## 46.71429  5.223919  4.321543
## 46.85714  8.812703  7.849078
## 47.00000 10.265017  9.286567
## 47.14286  8.320234  7.224785
## 47.28571  5.262556  4.073882
## 47.42857  8.498245  7.242634
## 47.57143  9.867371  8.600902
## 47.71429  6.718898  5.420905
## 47.85714  6.454510  5.083123
## 48.00000  9.010863  7.607488
## 
## $岩手県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286  9.143043  9.886188
## 46.28571 10.013142 10.848068
## 46.42857 15.781282 16.658768
## 46.57143 13.727548 14.606324
## 46.71429  8.633176  9.535552
## 46.85714 12.453367 13.416992
## 47.00000 13.961687 14.940136
## 47.14286 12.458938 13.554386
## 47.28571  9.753473 10.942147
## 47.42857 13.242060 14.497672
## 47.57143 14.652202 15.918671
## 47.71429 11.622831 12.920824
## 47.85714 11.635730 13.007116
## 48.00000 14.312943 15.716318
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 14.31916 15.75016 14.33155 15.44244 14.55941 15.18346 15.09679 15.25867
##  [9] 15.10559 15.00951 15.27266 14.91651 15.30435 14.93122
## 
## $宮城県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%      95%
## 46.14286  9.213869 6.511291
## 46.28571 10.553181 7.802064
## 46.42857  8.919627 6.054728
## 46.57143  9.998336 7.116403
## 46.71429  8.858578 5.840738
## 46.85714  9.454403 6.421621
## 47.00000  9.148437 5.999568
## 47.14286  9.182385 5.965793
## 47.28571  8.847289 5.534347
## 47.42857  8.657567 5.295049
## 47.57143  8.794805 5.365638
## 47.71429  8.316261 4.822297
## 47.85714  8.605669 5.059601
## 48.00000  8.099170 4.482499
## 
## $宮城県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 19.42446 22.12703
## 46.28571 20.94715 23.69826
## 46.42857 19.74348 22.60837
## 46.57143 20.88654 23.76847
## 46.71429 20.26025 23.27809
## 46.85714 20.91253 23.94531
## 47.00000 21.04515 24.19402
## 47.14286 21.33496 24.55155
## 47.28571 21.36388 24.67682
## 47.42857 21.36146 24.72398
## 47.57143 21.75051 25.17967
## 47.71429 21.51677 25.01073
## 47.85714 22.00303 25.54910
## 48.00000 21.76328 25.37995
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 1.6039348 0.8038763 0.8038763 0.8038763 0.8038763 0.8038763 0.8038763
##  [8] 0.8038763 0.8038763 0.8038763 0.8038763 0.8038763 0.8038763 0.8038763
## 
## $秋田県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%        95%
## 46.14286  0.2669893 -0.4407468
## 46.28571 -0.5497253 -1.2662787
## 46.42857 -0.5515022 -1.2689962
## 46.57143 -0.5532768 -1.2717103
## 46.71429 -0.5550491 -1.2744207
## 46.85714 -0.5568191 -1.2771277
## 47.00000 -0.5585867 -1.2798311
## 47.14286 -0.5603521 -1.2825310
## 47.28571 -0.5621152 -1.2852274
## 47.42857 -0.5638760 -1.2879204
## 47.57143 -0.5656346 -1.2906098
## 47.71429 -0.5673909 -1.2932959
## 47.85714 -0.5691449 -1.2959785
## 48.00000 -0.5708968 -1.2986577
## 
## $秋田県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 2.940880 3.648616
## 46.28571 2.157478 2.874031
## 46.42857 2.159255 2.876749
## 46.57143 2.161029 2.879463
## 46.71429 2.162802 2.882173
## 46.85714 2.164572 2.884880
## 47.00000 2.166339 2.887584
## 47.14286 2.168105 2.890284
## 47.28571 2.169868 2.892980
## 47.42857 2.171629 2.895673
## 47.57143 2.173387 2.898362
## 47.71429 2.175143 2.901048
## 47.85714 2.176897 2.903731
## 48.00000 2.178649 2.906410
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1]  0.11183937  0.41704423  0.50877917  0.09641300  0.26649124  0.18488790
##  [7] -0.09460440 -0.09410986 -0.12102751 -0.13555662 -0.13907956 -0.13300750
## [13] -0.11874900 -0.09768288
## 
## $山形県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%       95%
## 46.14286 -0.8963028 -1.429981
## 46.28571 -0.6086394 -1.151604
## 46.42857 -0.5422341 -1.098607
## 46.57143 -0.9862596 -1.559392
## 46.71429 -0.8521240 -1.444283
## 46.85714 -0.9717939 -1.584104
## 47.00000 -1.2895140 -1.922061
## 47.14286 -1.2986421 -1.936283
## 47.28571 -1.3542603 -2.007094
## 47.42857 -1.3932973 -2.059105
## 47.57143 -1.4171720 -2.093753
## 47.71429 -1.4275180 -2.112790
## 47.85714 -1.4260972 -2.118165
## 48.00000 -1.4147250 -2.111925
## 
## $山形県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 1.119982 1.653660
## 46.28571 1.442728 1.985692
## 46.42857 1.559792 2.116165
## 46.57143 1.179086 1.752218
## 46.71429 1.385107 1.977266
## 46.85714 1.341570 1.953880
## 47.00000 1.100305 1.732852
## 47.14286 1.110422 1.748063
## 47.28571 1.112205 1.765039
## 47.42857 1.122184 1.787992
## 47.57143 1.139013 1.815594
## 47.71429 1.161503 1.846775
## 47.85714 1.188599 1.880667
## 48.00000 1.219359 1.916559
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 3.075753 3.075753 3.075753 3.075753 3.075753 3.075753 3.075753 3.075753
##  [9] 3.075753 3.075753 3.075753 3.075753 3.075753 3.075753
## 
## $福島県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                  80%        95%
## 46.14286  0.42957687 -0.9712243
## 46.28571  0.35745279 -1.0815286
## 46.42857  0.28719352 -1.1889809
## 46.57143  0.21866149 -1.2937915
## 46.71429  0.15173525 -1.3961464
## 46.85714  0.08630694 -1.4962104
## 47.00000  0.02228028 -1.5941307
## 47.14286 -0.04043113 -1.6900395
## 47.28571 -0.10190518 -1.7840560
## 47.42857 -0.16221232 -1.8762878
## 47.57143 -0.22141659 -1.9668329
## 47.71429 -0.27957638 -2.0557806
## 47.85714 -0.33674508 -2.1432126
## 48.00000 -0.39297171 -2.2292038
## 
## $福島県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 5.721930 7.122731
## 46.28571 5.794054 7.233035
## 46.42857 5.864313 7.340487
## 46.57143 5.932845 7.445298
## 46.71429 5.999771 7.547653
## 46.85714 6.065200 7.647717
## 47.00000 6.129226 7.745637
## 47.14286 6.191938 7.841546
## 47.28571 6.253412 7.935563
## 47.42857 6.313719 8.027794
## 47.57143 6.372923 8.118339
## 47.71429 6.431083 8.207287
## 47.85714 6.488252 8.294719
## 48.00000 6.544478 8.380710
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 38.63487 34.75754 35.42106 36.71041 35.96374 32.46293 33.64674 35.12902
##  [9] 34.69725 33.77364 34.08260 34.54520 34.39345 34.13258
## 
## $茨城県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 33.29976 30.47552
## 46.28571 28.91740 25.82581
## 46.42857 29.47909 26.33361
## 46.57143 30.48912 27.19576
## 46.71429 29.05350 25.39544
## 46.85714 25.16978 21.30902
## 47.00000 26.12331 22.14065
## 47.14286 27.07087 22.80514
## 47.28571 26.17432 21.66255
## 47.42857 24.91598 20.22702
## 47.57143 24.91439 20.06103
## 47.71429 25.00897 19.96079
## 47.85714 24.50545 19.27106
## 48.00000 23.93542 18.53738
## 
## $茨城県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 43.96998 46.79422
## 46.28571 40.59769 43.68928
## 46.42857 41.36302 44.50851
## 46.57143 42.93171 46.22506
## 46.71429 42.87398 46.53204
## 46.85714 39.75609 43.61685
## 47.00000 41.17018 45.15284
## 47.14286 43.18717 47.45290
## 47.28571 43.22019 47.73195
## 47.42857 42.63129 47.32025
## 47.57143 43.25081 48.10416
## 47.71429 44.08143 49.12961
## 47.85714 44.28145 49.51584
## 48.00000 44.32974 49.72778
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 10.964805  9.343209  8.677735 10.063977  7.067208  9.125195  9.181995
##  [8]  8.589450  8.577899  8.448011  8.440237  8.411553  8.408695  8.402315
## 
## $栃木県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 7.829445 6.169686
## 46.28571 6.161567 4.477307
## 46.42857 5.304514 3.518838
## 46.57143 6.641703 4.830060
## 46.71429 3.577631 1.730361
## 46.85714 5.589754 3.718205
## 47.00000 5.598341 3.701269
## 47.14286 4.783329 2.768490
## 47.28571 4.709940 2.662366
## 47.42857 4.494714 2.401965
## 47.57143 4.426733 2.302113
## 47.71429 4.334768 2.176649
## 47.85714 4.274052 2.085304
## 48.00000 4.209961 1.990662
## 
## $栃木県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 14.10016 15.75992
## 46.28571 12.52485 14.20911
## 46.42857 12.05096 13.83663
## 46.57143 13.48625 15.29789
## 46.71429 10.55678 12.40405
## 46.85714 12.66064 14.53218
## 47.00000 12.76565 14.66272
## 47.14286 12.39557 14.41041
## 47.28571 12.44586 14.49343
## 47.42857 12.40131 14.49406
## 47.57143 12.45374 14.57836
## 47.71429 12.48834 14.64646
## 47.85714 12.54334 14.73209
## 48.00000 12.59467 14.81397
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 14.571075 11.550997 12.527534 11.966507 11.457394 11.052760 10.701568
##  [8] 10.414194 10.169636  9.966895  9.795862  9.653249  9.533406  9.433218
## 
## $群馬県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%         95%
## 46.14286 9.970618  7.53528329
## 46.28571 6.160746  3.30731972
## 46.42857 6.913329  3.94134899
## 46.57143 6.290176  3.28530828
## 46.71429 5.407405  2.20473362
## 46.85714 4.902112  1.64615511
## 47.00000 4.388825  1.04706006
## 47.14286 4.015125  0.62766308
## 47.28571 3.678415  0.24217007
## 47.42857 3.409859 -0.06122673
## 47.57143 3.177209 -0.32649376
## 47.71429 2.984306 -0.54601929
## 47.85714 2.818756 -0.73576559
## 48.00000 2.678660 -0.89698638
## 
## $群馬県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 19.17153 21.60687
## 46.28571 16.94125 19.79467
## 46.42857 18.14174 21.11372
## 46.57143 17.64284 20.64771
## 46.71429 17.50738 20.71005
## 46.85714 17.20341 20.45937
## 47.00000 17.01431 20.35608
## 47.14286 16.81326 20.20072
## 47.28571 16.66086 20.09710
## 47.42857 16.52393 19.99502
## 47.57143 16.41452 19.91822
## 47.71429 16.32219 19.85252
## 47.85714 16.24806 19.80258
## 48.00000 16.18778 19.76342
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 100.42659  95.61260 129.30212 103.48807  92.98744  72.42371  96.92518
##  [8]  97.54934  95.44310 110.18315  98.88882  94.29452  85.29735  96.01739
## 
## $埼玉県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286  84.97132  76.78980
## 46.28571  79.15263  70.43924
## 46.42857 112.34125 103.36270
## 46.57143  86.04067  76.80457
## 46.71429  75.06671  65.58006
## 46.85714  54.04184  44.31107
## 47.00000  78.09346  68.12455
## 47.14286  75.81379  64.30768
## 47.28571  72.73655  60.71644
## 47.42857  86.72721  74.31038
## 47.57143  74.70670  61.90545
## 47.71429  69.40739  56.23294
## 47.85714  59.72464  46.18726
## 48.00000  69.77701  55.88619
## 
## $埼玉県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%      95%
## 46.14286 115.88185 124.0634
## 46.28571 112.07257 120.7860
## 46.42857 146.26299 155.2415
## 46.57143 120.93547 130.1716
## 46.71429 110.90817 120.3948
## 46.85714  90.80557 100.5363
## 47.00000 115.75689 125.7258
## 47.14286 119.28490 130.7910
## 47.28571 118.14964 130.1698
## 47.42857 133.63909 146.0559
## 47.57143 123.07095 135.8722
## 47.71429 119.18166 132.3561
## 47.85714 110.87006 124.4074
## 48.00000 122.25777 136.1486
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 82.58171 76.88288 83.17689 74.39077 72.60210 68.09195 72.19824 78.53552
##  [9] 76.19517 78.05589 74.31442 70.21224 67.02228 72.42120
## 
## $千葉県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 70.23185 63.69423
## 46.28571 63.07552 55.76634
## 46.42857 68.82490 61.22741
## 46.57143 59.51407 51.63883
## 46.71429 57.21859 49.07505
## 46.85714 52.21780 43.81453
## 47.00000 55.84816 47.19295
## 47.14286 60.88327 51.53873
## 47.28571 57.76874 48.01438
## 47.42857 59.01847 48.94067
## 47.57143 54.68501 44.29383
## 47.71429 50.00819 39.31281
## 47.85714 46.25949 35.26833
## 48.00000 51.11431 39.83512
## 
## $千葉県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 94.93157 101.46919
## 46.28571 90.69025  97.99943
## 46.42857 97.52889 105.12637
## 46.57143 89.26746  97.14271
## 46.71429 87.98561  96.12915
## 46.85714 83.96611  92.36938
## 47.00000 88.54832  97.20353
## 47.14286 96.18776 105.53230
## 47.28571 94.62160 104.37596
## 47.42857 97.09331 107.17111
## 47.57143 93.94382 104.33500
## 47.71429 90.41629 101.11166
## 47.85714 87.78508  98.77624
## 48.00000 93.72810 105.00729
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 475.4039 455.5576 472.9756 352.9732 290.1075 296.3683 401.0898 473.5559
##  [9] 449.8162 465.1535 350.2072 289.1360 321.3272 409.1325
## 
## $東京都$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 424.7367 397.9151
## 46.28571 397.0971 366.1500
## 46.42857 410.5711 377.5361
## 46.57143 287.7125 253.1656
## 46.71429 222.4492 186.6331
## 46.85714 226.5822 189.6397
## 47.00000 329.3748 291.4111
## 47.14286 393.0285 350.3999
## 47.28571 364.7579 319.7307
## 47.42857 376.7897 330.0128
## 47.57143 259.0539 210.8002
## 47.71429 195.4961 145.9261
## 47.85714 225.4249 174.6572
## 48.00000 311.1543 259.2877
## 
## $東京都$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 526.0712 552.8928
## 46.28571 514.0180 544.9651
## 46.42857 535.3801 568.4151
## 46.57143 418.2339 452.7808
## 46.71429 357.7658 393.5819
## 46.85714 366.1544 403.0970
## 47.00000 472.8049 510.7685
## 47.14286 554.0833 596.7120
## 47.28571 534.8744 579.9016
## 47.42857 553.5172 600.2941
## 47.57143 441.3605 489.6142
## 47.71429 382.7759 432.3459
## 47.85714 417.2295 467.9972
## 48.00000 507.1108 558.9773
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 177.9951 198.4778 188.0060 165.6767 109.2385 105.5719 156.7670 167.4897
##  [9] 179.5321 174.1511 161.9072 131.2542 129.1158 156.6315
## 
## $神奈川県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 156.62772 145.31650
## 46.28571 173.81460 160.75870
## 46.42857 161.94871 148.15479
## 46.57143 139.47940 125.61136
## 46.71429  82.93682  69.01356
## 46.85714  79.09117  65.07314
## 47.00000 129.90352 115.68286
## 47.14286 135.88999 119.16212
## 47.28571 145.78289 127.91711
## 47.42857 139.09554 120.53824
## 47.57143 126.31136 107.46806
## 47.71429  95.17636  76.07789
## 47.85714  92.49384  73.10735
## 48.00000 119.32350  99.57382
## 
## $神奈川県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 199.3625 210.6738
## 46.28571 223.1410 236.1969
## 46.42857 214.0634 227.8573
## 46.57143 191.8741 205.7421
## 46.71429 135.5402 149.4634
## 46.85714 132.0526 146.0706
## 47.00000 183.6304 197.8511
## 47.14286 199.0894 215.8173
## 47.28571 213.2814 231.1472
## 47.42857 209.2067 227.7640
## 47.57143 197.5030 216.3463
## 47.71429 167.3321 186.4305
## 47.85714 165.7377 185.1242
## 48.00000 193.9396 213.6892
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 7.825437 7.825437 7.825437 7.825437 7.825437 7.825437 7.825437 7.825437
##  [9] 7.825437 7.825437 7.825437 7.825437 7.825437 7.825437
## 
## $新潟県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 4.818649 3.226951
## 46.28571 4.775205 3.160510
## 46.42857 4.732371 3.095001
## 46.57143 4.690122 3.030387
## 46.71429 4.648436 2.966633
## 46.85714 4.607289 2.903704
## 47.00000 4.566661 2.841569
## 47.14286 4.526534 2.780200
## 47.28571 4.486889 2.719569
## 47.42857 4.447710 2.659649
## 47.57143 4.408979 2.600416
## 47.71429 4.370683 2.541847
## 47.85714 4.332807 2.483920
## 48.00000 4.295337 2.426615
## 
## $新潟県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 10.83223 12.42392
## 46.28571 10.87567 12.49036
## 46.42857 10.91850 12.55587
## 46.57143 10.96075 12.62049
## 46.71429 11.00244 12.68424
## 46.85714 11.04359 12.74717
## 47.00000 11.08421 12.80930
## 47.14286 11.12434 12.87067
## 47.28571 11.16398 12.93131
## 47.42857 11.20316 12.99123
## 47.57143 11.24189 13.05046
## 47.71429 11.28019 13.10903
## 47.85714 11.31807 13.16695
## 48.00000 11.35554 13.22426
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 2.032934 2.000777 1.970223 1.941190 1.913605 1.887394 1.862489 1.838825
##  [9] 1.816340 1.794975 1.774675 1.755387 1.737059 1.719645
## 
## $富山県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%       95%
## 46.14286 -0.4138047 -1.709030
## 46.28571 -0.5999896 -1.976752
## 46.42857 -0.7621573 -2.208592
## 46.57143 -0.9047903 -2.411362
## 46.71429 -1.0311762 -2.590049
## 46.85714 -1.1438222 -2.748451
## 47.00000 -1.2446987 -2.889545
## 47.14286 -1.3353906 -3.015719
## 47.28571 -1.4171971 -3.128928
## 47.42857 -1.4911992 -3.230795
## 47.57143 -1.5583080 -3.322683
## 47.71429 -1.6192992 -3.405750
## 47.85714 -1.6748393 -3.480989
## 48.00000 -1.7255050 -3.549257
## 
## $富山県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 4.479672 5.774897
## 46.28571 4.601544 5.978306
## 46.42857 4.702602 6.149037
## 46.57143 4.787171 6.293742
## 46.71429 4.858386 6.417259
## 46.85714 4.918610 6.523239
## 47.00000 4.969677 6.614522
## 47.14286 5.013040 6.693369
## 47.28571 5.049877 6.761608
## 47.42857 5.081150 6.820745
## 47.57143 5.107658 6.872033
## 47.71429 5.130072 6.916523
## 47.85714 5.148957 6.955107
## 48.00000 5.164794 6.988547
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 1.277625 1.277625 1.277625 1.277625 1.277625 1.277625 1.277625 1.277625
##  [9] 1.277625 1.277625 1.277625 1.277625 1.277625 1.277625
## 
## $石川県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 -2.347559 -4.266615
## 46.28571 -2.550673 -4.577252
## 46.42857 -2.743541 -4.872218
## 46.57143 -2.927573 -5.153670
## 46.71429 -3.103882 -5.423311
## 46.85714 -3.273365 -5.682513
## 47.00000 -3.436759 -5.932404
## 47.14286 -3.594677 -6.173918
## 47.28571 -3.747635 -6.407847
## 47.42857 -3.896073 -6.634863
## 47.57143 -4.040369 -6.855545
## 47.71429 -4.180852 -7.070395
## 47.85714 -4.317809 -7.279853
## 48.00000 -4.451493 -7.484305
## 
## $石川県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 4.902809  6.821865
## 46.28571 5.105923  7.132502
## 46.42857 5.298791  7.427468
## 46.57143 5.482823  7.708920
## 46.71429 5.659131  7.978561
## 46.85714 5.828615  8.237763
## 47.00000 5.992009  8.487654
## 47.14286 6.149927  8.729168
## 47.28571 6.302885  8.963097
## 47.42857 6.451323  9.190113
## 47.57143 6.595619  9.410795
## 47.71429 6.736102  9.625645
## 47.85714 6.873059  9.835103
## 48.00000 7.006743 10.039555
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 0.9499559 0.9531479 0.9526842 0.9522912 0.9519581 0.9516758 0.9514366
##  [8] 0.9512338 0.9510620 0.9509164 0.9507930 0.9506884 0.9505997 0.9505246
## 
## $福井県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 -1.054673 -2.115860
## 46.28571 -1.188297 -2.321910
## 46.42857 -1.412652 -2.664785
## 46.57143 -1.561577 -2.892339
## 46.71429 -1.663392 -3.047874
## 46.85714 -1.734196 -3.156011
## 47.00000 -1.783965 -3.232000
## 47.14286 -1.819196 -3.285773
## 47.28571 -1.844255 -3.324007
## 47.42857 -1.862140 -3.351282
## 47.57143 -1.874935 -3.370786
## 47.71429 -1.884107 -3.384756
## 47.85714 -1.890689 -3.394777
## 48.00000 -1.895420 -3.401971
## 
## $福井県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 2.954585 4.015771
## 46.28571 3.094593 4.228206
## 46.42857 3.318020 4.570154
## 46.57143 3.466160 4.796921
## 46.71429 3.567308 4.951790
## 46.85714 3.637548 5.059363
## 47.00000 3.686839 5.134873
## 47.14286 3.721664 5.188240
## 47.28571 3.746379 5.226131
## 47.42857 3.763973 5.253115
## 47.57143 3.776521 5.272372
## 47.71429 3.785483 5.286133
## 47.85714 3.791889 5.295976
## 48.00000 3.796469 5.303021
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 3.552797 4.366849 4.626439 4.709219 4.735616 4.744034 4.746718 4.747574
##  [9] 4.747847 4.747934 4.747962 4.747971 4.747974 4.747975
## 
## $山梨県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 1.658720 0.6560556
## 46.28571 2.279146 1.1739820
## 46.42857 2.466466 1.3230449
## 46.57143 2.502717 1.3346655
## 46.71429 2.489909 1.3011035
## 46.85714 2.461693 1.2534940
## 47.00000 2.428908 1.2019328
## 47.14286 2.395012 1.1496412
## 47.28571 2.361097 1.0976282
## 47.42857 2.327497 1.0461954
## 47.57143 2.294306 0.9954192
## 47.71429 2.261542 0.9453055
## 47.85714 2.229198 0.8958388
## 48.00000 2.197263 0.8469977
## 
## $山梨県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 5.446875 6.449539
## 46.28571 6.454553 7.559716
## 46.42857 6.786413 7.929834
## 46.57143 6.915721 8.083772
## 46.71429 6.981323 8.170129
## 46.85714 7.026375 8.234574
## 47.00000 7.064529 8.291504
## 47.14286 7.100136 8.345507
## 47.28571 7.134597 8.398066
## 47.42857 7.168371 8.449673
## 47.57143 7.201618 8.500505
## 47.71429 7.234400 8.550636
## 47.85714 7.266749 8.600108
## 48.00000 7.298686 8.648951
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 11.94519 11.94519 11.94519 11.94519 11.94519 11.94519 11.94519 11.94519
##  [9] 11.94519 11.94519 11.94519 11.94519 11.94519 11.94519
## 
## $長野県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 8.556045 6.761941
## 46.28571 8.272042 6.327596
## 46.42857 8.008475 5.924504
## 46.57143 7.761479 5.546757
## 46.71429 7.528274 5.190100
## 46.85714 7.306779 4.851353
## 47.00000 7.095389 4.528060
## 47.14286 6.892836 4.218282
## 47.28571 6.698096 3.920453
## 47.42857 6.510330 3.633289
## 47.57143 6.328838 3.355720
## 47.71429 6.153029 3.086845
## 47.85714 5.982402 2.825893
## 48.00000 5.816524 2.572204
## 
## $長野県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 15.33433 17.12844
## 46.28571 15.61834 17.56278
## 46.42857 15.88190 17.96587
## 46.57143 16.12890 18.34362
## 46.71429 16.36210 18.70028
## 46.85714 16.58360 19.03903
## 47.00000 16.79499 19.36232
## 47.14286 16.99754 19.67210
## 47.28571 17.19228 19.96993
## 47.42857 17.38005 20.25709
## 47.57143 17.56154 20.53466
## 47.71429 17.73735 20.80353
## 47.85714 17.90798 21.06449
## 48.00000 18.07385 21.31817
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 21.30601 23.52637 20.62052 17.22796 17.07883 16.42998 21.03439 20.66748
##  [9] 21.80893 21.24942 20.74989 19.99835 19.60891 19.54202
## 
## $岐阜県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 17.57472 15.599497
## 46.28571 19.44461 17.283861
## 46.42857 16.12960 13.752250
## 46.57143 12.61858 10.178519
## 46.71429 12.36028  9.862426
## 46.85714 11.60349  9.048491
## 47.00000 16.03288 13.385235
## 47.14286 15.19891 12.304022
## 47.28571 15.97060 12.879974
## 47.42857 15.04255 11.756832
## 47.57143 14.26702 10.835200
## 47.71429 13.29154  9.741178
## 47.85714 12.71487  9.065391
## 48.00000 12.46682  8.721438
## 
## $岐阜県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 25.03730 27.01252
## 46.28571 27.60812 29.76888
## 46.42857 25.11143 27.48878
## 46.57143 21.83735 24.27741
## 46.71429 21.79739 24.29524
## 46.85714 21.25648 23.81148
## 47.00000 26.03590 28.68355
## 47.14286 26.13606 29.03094
## 47.28571 27.64726 30.73788
## 47.42857 27.45630 30.74202
## 47.57143 27.23276 30.66458
## 47.71429 26.70515 30.25552
## 47.85714 26.50294 30.15242
## 48.00000 26.61722 30.36261
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 51.14847 51.71900 50.90036 46.73533 48.74172 42.38127 44.33569 45.40379
##  [9] 45.78662 45.92383 45.97301 45.99064 45.99695 45.99922
## 
## $静岡県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 44.41083 40.84414
## 46.28571 43.93918 39.82079
## 46.42857 42.60840 38.21891
## 46.57143 38.07380 33.48866
## 46.71429 39.76127 35.00731
## 46.85714 33.10474 28.19404
## 47.00000 34.77632 29.71590
## 47.14286 34.87231 29.29729
## 47.28571 34.68174 28.80318
## 47.42857 34.36690 28.24902
## 47.57143 34.01129 27.67913
## 47.71429 33.64763 27.11364
## 47.85714 33.28764 26.55974
## 48.00000 32.93509 26.01935
## 
## $静岡県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 57.88610 61.45279
## 46.28571 59.49882 63.61721
## 46.42857 59.19232 63.58182
## 46.57143 55.39687 59.98200
## 46.71429 57.72216 62.47613
## 46.85714 51.65780 56.56850
## 47.00000 53.89506 58.95548
## 47.14286 55.93527 61.51030
## 47.28571 56.89149 62.77006
## 47.42857 57.48077 63.59864
## 47.57143 57.93473 64.26689
## 47.71429 58.33364 64.86763
## 47.85714 58.70627 65.43417
## 48.00000 59.06335 65.97908
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 202.6998 199.4861 202.6019 162.8588 132.9809 142.1330 182.6144 198.1617
##  [9] 196.2157 198.1005 174.0712 156.0069 161.5403 186.0157
## 
## $愛知県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 185.42658 176.28267
## 46.28571 178.68659 167.67600
## 46.42857 177.82771 164.71306
## 46.57143 134.95375 120.18173
## 46.71429 102.17715  85.87061
## 46.85714 108.70672  91.01191
## 47.00000 146.74894 127.76290
## 47.14286 155.31758 132.63725
## 47.28571 148.93284 123.90279
## 47.42857 146.31013 118.89395
## 47.57143 118.27497  88.73823
## 47.71429  96.43094  64.89331
## 47.85714  98.42381  65.01194
## 48.00000 119.54284  84.35422
## 
## $愛知県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 219.9731 229.1170
## 46.28571 220.2856 231.2962
## 46.42857 227.3760 240.4907
## 46.57143 190.7638 205.5358
## 46.71429 163.7847 180.0912
## 46.85714 175.5593 193.2541
## 47.00000 218.4799 237.4660
## 47.14286 241.0059 263.6862
## 47.28571 243.4986 268.5287
## 47.42857 249.8909 277.3071
## 47.57143 229.8674 259.4042
## 47.71429 215.5829 247.1206
## 47.85714 224.6568 258.0687
## 48.00000 252.4886 287.6772
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 14.86833 14.54404 14.95031 14.20506 13.80600 13.08525 14.38082 13.99294
##  [9] 13.99294 13.99294 13.99294 13.99294 13.99294 13.99294
## 
## $三重県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%      95%
## 46.14286 10.862130 8.741374
## 46.28571 10.198482 7.898083
## 46.42857 10.290049 7.823054
## 46.57143  9.250040 6.627009
## 46.71429  8.572789 5.802496
## 46.85714  7.587916 4.677805
## 47.00000  8.631486 5.587972
## 47.14286  7.865724 4.622172
## 47.28571  7.587800 4.197124
## 47.42857  7.321444 3.789767
## 47.57143  7.065321 3.398061
## 47.71429  6.818336 3.020330
## 47.85714  6.579575 2.655176
## 48.00000  6.348267 2.301421
## 
## $三重県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 18.87454 20.99529
## 46.28571 18.88959 21.18999
## 46.42857 19.61058 22.07757
## 46.57143 19.16009 21.78312
## 46.71429 19.03920 21.80950
## 46.85714 18.58258 21.49269
## 47.00000 20.13016 23.17367
## 47.14286 20.12016 23.36371
## 47.28571 20.39808 23.78876
## 47.42857 20.66444 24.19611
## 47.57143 20.92056 24.58782
## 47.71429 21.16754 24.96555
## 47.85714 21.40631 25.33070
## 48.00000 21.63761 25.68446
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 7.912200 8.503089 8.181662 8.356234 8.261257 8.312832 8.284767 8.300003
##  [9] 8.291710 8.296211 8.293761 8.295090 8.294366 8.294759
## 
## $滋賀県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%        95%
## 46.14286 4.017931 1.95643032
## 46.28571 4.534211 2.43321400
## 46.42857 3.798173 1.47769438
## 46.57143 3.880643 1.51140739
## 46.71429 3.625796 1.17193171
## 46.85714 3.588505 1.08759744
## 47.00000 3.456886 0.90116018
## 47.14286 3.388369 0.78830728
## 47.28571 3.293732 0.64796293
## 47.42857 3.218472 0.53047984
## 47.57143 3.136529 0.40645563
## 47.71429 3.061113 0.29041236
## 47.85714 2.984532 0.17367516
## 48.00000 2.910609 0.06041201
## 
## $滋賀県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 11.80647 13.86797
## 46.28571 12.47197 14.57296
## 46.42857 12.56515 14.88563
## 46.57143 12.83183 15.20106
## 46.71429 12.89672 15.35058
## 46.85714 13.03716 15.53807
## 47.00000 13.11265 15.66837
## 47.14286 13.21164 15.81170
## 47.28571 13.28969 15.93546
## 47.42857 13.37395 16.06194
## 47.57143 13.45099 16.18107
## 47.71429 13.52907 16.29977
## 47.85714 13.60420 16.41506
## 48.00000 13.67891 16.52911
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 24.32707 25.01823 25.84253 23.84582 21.29544 26.78697 28.00366 23.04923
##  [9] 24.21416 26.09423 24.10883 22.74927 21.60244 24.94678
## 
## $京都府$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 17.35909 13.670463
## 46.28571 17.42842 13.410617
## 46.42857 18.11109 14.018312
## 46.57143 15.97530 11.808895
## 46.71429 13.28825  9.049505
## 46.85714 18.64541 14.335535
## 47.00000 19.72992 15.350065
## 47.14286 14.38900  9.804549
## 47.28571 15.33894 10.640679
## 47.42857 17.05719 12.273272
## 47.57143 14.91283 10.044759
## 47.71429 13.39701  8.446214
## 47.85714 12.09648  7.064326
## 48.00000 15.28957 10.177349
## 
## $京都府$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 31.29504 34.98367
## 46.28571 32.60805 36.62585
## 46.42857 33.57397 37.66675
## 46.57143 31.71634 35.88274
## 46.71429 29.30262 33.54137
## 46.85714 34.92852 39.23840
## 47.00000 36.27741 40.65726
## 47.14286 31.70946 36.29391
## 47.28571 33.08938 37.78764
## 47.42857 35.13126 39.91518
## 47.57143 33.30483 38.17290
## 47.71429 32.10154 37.05233
## 47.85714 31.10841 36.14056
## 48.00000 34.60400 39.71622
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 411.5441 433.2447 458.0926 485.6798 351.0661 359.5895 410.5053 463.9692
##  [9] 481.8633 502.3049 524.9635 414.9767 422.0138 463.7016
## 
## $大阪府$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 379.4169 362.4098
## 46.28571 397.1604 378.0586
## 46.42857 421.4658 402.0767
## 46.57143 448.1640 428.3044
## 46.71429 312.3366 291.8344
## 46.85714 319.3695 298.0784
## 47.00000 368.5757 346.3795
## 47.14286 413.6976 387.0855
## 47.28571 427.3648 398.5150
## 47.42857 445.2653 415.0705
## 47.57143 465.1077 433.4220
## 47.71429 352.1124 318.8341
## 47.85714 356.0175 321.0812
## 48.00000 394.5037 357.8726
## 
## $大阪府$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 443.6714 460.6785
## 46.28571 469.3290 488.4308
## 46.42857 494.7195 514.1086
## 46.57143 523.1956 543.0553
## 46.71429 389.7957 410.2979
## 46.85714 399.8095 421.1007
## 47.00000 452.4349 474.6311
## 47.14286 514.2408 540.8530
## 47.28571 536.3619 565.2117
## 47.42857 559.3444 589.5393
## 47.57143 584.8193 616.5051
## 47.71429 477.8409 511.1193
## 47.85714 488.0100 522.9463
## 48.00000 532.8994 569.5305
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 116.71528 120.31790 133.38672 127.07278  94.81278  95.17340 108.09392
##  [8] 116.54207 118.52315 125.48858 122.16955 105.09685 105.29670 112.14573
## 
## $兵庫県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 105.37117  99.36596
## 46.28571 107.58526 100.84501
## 46.42857 119.43898 112.05550
## 46.57143 112.31908 104.50894
## 46.71429  79.38667  71.22058
## 46.85714  79.16763  70.69469
## 47.00000  91.56047  82.80819
## 47.14286  97.18069  86.93139
## 47.28571  97.90642  86.99258
## 47.42857 103.71238  92.18475
## 47.57143  99.43622  87.40192
## 47.71429  81.49163  68.99579
## 47.85714  80.88431  67.96118
## 48.00000  86.96906  73.64134
## 
## $兵庫県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 128.0594 134.0646
## 46.28571 133.0505 139.7908
## 46.42857 147.3345 154.7179
## 46.57143 141.8265 149.6366
## 46.71429 110.2389 118.4050
## 46.85714 111.1792 119.6521
## 47.00000 124.6274 133.3797
## 47.14286 135.9035 146.1528
## 47.28571 139.1399 150.0537
## 47.42857 147.2648 158.7924
## 47.57143 144.9029 156.9372
## 47.71429 128.7021 141.1979
## 47.85714 129.7091 142.6322
## 48.00000 137.3224 150.6501
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 17.51240 16.52176 16.42284 15.54331 16.44133 15.84834 15.24898 15.87265
##  [9] 15.87265 15.87265 15.87265 15.87265 15.87265 15.87265
## 
## $奈良県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 13.175342 10.879444
## 46.28571 11.991282  9.592992
## 46.42857 11.706862  9.210376
## 46.57143 10.648865  8.057902
## 46.71429 11.374696  8.692582
## 46.85714 10.615180  7.844912
## 47.00000  9.854428  6.998726
## 47.14286 10.200189  7.197373
## 47.28571 10.019341  6.920789
## 47.42857  9.843915  6.652498
## 47.57143  9.673451  6.391796
## 47.71429  9.507551  6.138074
## 47.85714  9.345866  5.890798
## 48.00000  9.188091  5.649502
## 
## $奈良県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 21.84945 24.14535
## 46.28571 21.05224 23.45053
## 46.42857 21.13881 23.63530
## 46.57143 20.43776 23.02872
## 46.71429 21.50797 24.19008
## 46.85714 21.08150 23.85177
## 47.00000 20.64353 23.49923
## 47.14286 21.54510 24.54792
## 47.28571 21.72595 24.82450
## 47.42857 21.90138 25.09279
## 47.57143 22.07184 25.35349
## 47.71429 22.23774 25.60722
## 47.85714 22.39942 25.85449
## 48.00000 22.55720 26.09579
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 6.689386 6.501496 7.590215 7.346155 7.024758 7.061159 7.756551 7.092533
##  [9] 7.456448 7.146209 7.410688 7.185219 7.377432 7.213570
## 
## $和歌山県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 4.625062 3.532274
## 46.28571 4.244190 3.049244
## 46.42857 5.261473 4.028712
## 46.57143 4.862663 3.547982
## 46.71429 4.466196 3.111775
## 46.85714 4.372441 2.949119
## 47.00000 4.991227 3.527354
## 47.14286 4.279613 2.790544
## 47.28571 4.591575 3.075003
## 47.42857 4.196665 2.635270
## 47.57143 4.407234 2.817301
## 47.71429 4.105368 2.474994
## 47.85714 4.242795 2.583419
## 48.00000 4.008707 2.312155
## 
## $和歌山県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286  8.753710  9.846498
## 46.28571  8.758803  9.953748
## 46.42857  9.918956 11.151718
## 46.57143  9.829647 11.144329
## 46.71429  9.583320 10.937741
## 46.85714  9.749878 11.173199
## 47.00000 10.521874 11.985747
## 47.14286  9.905453 11.394522
## 47.28571 10.321320 11.837892
## 47.42857 10.095754 11.657149
## 47.57143 10.414143 12.004075
## 47.71429 10.265070 11.895445
## 47.85714 10.512069 12.171446
## 48.00000 10.418434 12.114986
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 0.3122400 0.3805586 0.2770070 0.3190622 0.3220830 0.3120290 0.3174376
##  [8] 0.3173576 0.3164958 0.3171896 0.3171613 0.3171129 0.3172077 0.3172097
## 
## $鳥取県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%       95%
## 46.14286 -0.6176727 -1.109939
## 46.28571 -0.5586503 -1.055837
## 46.42857 -0.6730133 -1.175924
## 46.57143 -0.6491130 -1.161634
## 46.71429 -0.6488705 -1.162862
## 46.85714 -0.6611540 -1.176326
## 47.00000 -0.6574466 -1.173519
## 47.14286 -0.6582875 -1.174763
## 47.28571 -0.6597387 -1.176526
## 47.42857 -0.6594980 -1.176525
## 47.57143 -0.6598662 -1.177073
## 47.71429 -0.6602108 -1.177575
## 47.85714 -0.6603801 -1.177884
## 48.00000 -0.6606197 -1.178251
## 
## $鳥取県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 1.242153 1.734419
## 46.28571 1.319767 1.816955
## 46.42857 1.227027 1.729938
## 46.57143 1.287237 1.799759
## 46.71429 1.293036 1.807028
## 46.85714 1.285212 1.800384
## 47.00000 1.292322 1.808394
## 47.14286 1.293003 1.809478
## 47.28571 1.292730 1.809518
## 47.42857 1.293877 1.810904
## 47.57143 1.294189 1.811396
## 47.71429 1.294437 1.811800
## 47.85714 1.294795 1.812299
## 48.00000 1.295039 1.812671
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 0.4525316 0.4525316 0.4525316 0.4525316 0.4525316 0.4525316 0.4525316
##  [8] 0.4525316 0.4525316 0.4525316 0.4525316 0.4525316 0.4525316 0.4525316
## 
## $島根県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 -6.211023 -9.738496
## 46.28571 -6.211023 -9.738496
## 46.42857 -6.211023 -9.738496
## 46.57143 -6.211023 -9.738496
## 46.71429 -6.211023 -9.738496
## 46.85714 -6.211023 -9.738496
## 47.00000 -6.211023 -9.738496
## 47.14286 -6.211023 -9.738496
## 47.28571 -6.211023 -9.738496
## 47.42857 -6.211023 -9.738496
## 47.57143 -6.211023 -9.738496
## 47.71429 -6.211023 -9.738496
## 47.85714 -6.211023 -9.738496
## 48.00000 -6.211023 -9.738496
## 
## $島根県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 7.116087 10.64356
## 46.28571 7.116087 10.64356
## 46.42857 7.116087 10.64356
## 46.57143 7.116087 10.64356
## 46.71429 7.116087 10.64356
## 46.85714 7.116087 10.64356
## 47.00000 7.116087 10.64356
## 47.14286 7.116087 10.64356
## 47.28571 7.116087 10.64356
## 47.42857 7.116087 10.64356
## 47.57143 7.116087 10.64356
## 47.71429 7.116087 10.64356
## 47.85714 7.116087 10.64356
## 48.00000 7.116087 10.64356
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 12.45597 11.79867 15.27295 11.70033 11.77316 10.80846 11.86647 11.77955
##  [9] 11.81510 11.83815 11.85309 11.86278 11.86906 11.87313
## 
## $岡山県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%      95%
## 46.14286  9.263613 7.573679
## 46.28571  8.394243 6.592049
## 46.42857 11.734829 9.861861
## 46.57143  8.068154 6.145396
## 46.71429  8.068542 6.107437
## 46.85714  7.043877 5.051026
## 47.00000  8.049446 6.028837
## 47.14286  7.726014 5.580203
## 47.28571  7.669190 5.474478
## 47.42857  7.614314 5.378350
## 47.57143  7.560192 5.287668
## 47.71429  7.506467 5.200374
## 47.85714  7.453076 5.115395
## 48.00000  7.400050 5.032143
## 
## $岡山県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 15.64833 17.33827
## 46.28571 15.20309 17.00529
## 46.42857 18.81107 20.68404
## 46.57143 15.33251 17.25526
## 46.71429 15.47778 17.43888
## 46.85714 14.57305 16.56590
## 47.00000 15.68349 17.70410
## 47.14286 15.83308 17.97889
## 47.28571 15.96101 18.15572
## 47.42857 16.06198 18.29795
## 47.57143 16.14599 18.41852
## 47.71429 16.21909 18.52518
## 47.85714 16.28504 18.62272
## 48.00000 16.34621 18.71412
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 7.400742 7.039101 4.288191 5.078031 6.193216 5.349428 6.725507 5.427202
##  [9] 6.142806 6.229772 6.225219 6.696991 6.193640 5.338146
## 
## $広島県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%        95%
## 46.14286  3.7971136  1.8894680
## 46.28571  3.0834777  0.9894975
## 46.42857  0.1467570 -2.0455855
## 46.57143  0.2969089 -2.2340644
## 46.71429  0.8475284 -1.9823078
## 46.85714 -0.2756781 -3.2534296
## 47.00000  0.7973080 -2.3408910
## 47.14286 -0.7933183 -4.0862630
## 47.28571 -0.3698780 -3.8174846
## 47.42857 -0.5290493 -4.1069529
## 47.57143 -0.7707431 -4.4741817
## 47.71429 -0.5453902 -4.3792751
## 47.85714 -1.2840496 -5.2424995
## 48.00000 -2.3593777 -6.4342008
## 
## $広島県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%      95%
## 46.14286 11.004370 12.91202
## 46.28571 10.994723 13.08870
## 46.42857  8.429624 10.62197
## 46.57143  9.859154 12.39013
## 46.71429 11.538904 14.36874
## 46.85714 10.974535 13.95229
## 47.00000 12.653706 15.79191
## 47.14286 11.647723 14.94067
## 47.28571 12.655490 16.10310
## 47.42857 12.988592 16.56650
## 47.57143 13.221181 16.92462
## 47.71429 13.939372 17.77326
## 47.85714 13.671330 17.62978
## 48.00000 13.035670 17.11049
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1]  8.406865 13.702166  9.773203  9.995863  6.197266  7.929094  7.253650
##  [8] 10.393504  9.498341 10.168025  8.028153  8.819550  6.897579  8.994299
## 
## $山口県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%      95%
## 46.14286  5.806208 4.429503
## 46.28571 10.832899 9.314000
## 46.42857  6.733040 5.123674
## 46.57143  6.903725 5.266846
## 46.71429  3.020710 1.339143
## 46.85714  4.719384 3.020266
## 47.00000  3.973808 2.237564
## 47.14286  6.932048 5.099664
## 47.28571  5.904258 4.001664
## 47.42857  6.505376 4.566487
## 47.57143  4.280022 2.295880
## 47.71429  5.017094 3.004196
## 47.85714  3.019224 0.966147
## 48.00000  5.064699 2.984495
## 
## $山口県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%      95%
## 46.14286 11.007523 12.38423
## 46.28571 16.571434 18.09033
## 46.42857 12.813367 14.42273
## 46.57143 13.088001 14.72488
## 46.71429  9.373823 11.05539
## 46.85714 11.138804 12.83792
## 47.00000 10.533493 12.26974
## 47.14286 13.854959 15.68734
## 47.28571 13.092425 14.99502
## 47.42857 13.830673 15.76956
## 47.57143 11.776285 13.76043
## 47.71429 12.622007 14.63491
## 47.85714 10.775934 12.82901
## 48.00000 12.923898 15.00410
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 0.6415121 0.9431119 0.6855404 0.7481220 0.6080584 0.2897512 0.3622186
##  [8] 0.4880715 0.4400333 0.4251088 0.5190031 0.6380288 0.6734025 0.6922252
## 
## $徳島県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%       95%
## 46.14286 -1.0675443 -1.972264
## 46.28571 -0.8494205 -1.798330
## 46.42857 -1.1253155 -2.083925
## 46.57143 -1.0808740 -2.049086
## 46.71429 -1.2388993 -2.216620
## 46.85714 -1.5749954 -2.562133
## 47.00000 -1.5201487 -2.516614
## 47.14286 -1.4860430 -2.531076
## 47.28571 -1.5673300 -2.629964
## 47.42857 -1.6057091 -2.680759
## 47.57143 -1.5350015 -2.622326
## 47.71429 -1.4389037 -2.538365
## 47.85714 -1.4262075 -2.537674
## 48.00000 -1.4298201 -2.553163
## 
## $徳島県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 2.350569 3.255288
## 46.28571 2.735644 3.684554
## 46.42857 2.496396 3.455006
## 46.57143 2.577118 3.545330
## 46.71429 2.455016 3.432737
## 46.85714 2.154498 3.141635
## 47.00000 2.244586 3.241051
## 47.14286 2.462186 3.507219
## 47.28571 2.447397 3.510030
## 47.42857 2.455927 3.530977
## 47.57143 2.573008 3.660332
## 47.71429 2.714961 3.814423
## 47.85714 2.773013 3.884479
## 48.00000 2.814270 3.937613
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 0.9541159 1.1391372 1.1391372 1.1391372 1.1391372 1.1391372 1.1391372
##  [8] 1.1391372 1.1391372 1.1391372 1.1391372 1.1391372 1.1391372 1.1391372
## 
## $香川県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%       95%
## 46.14286 -0.4853421 -1.247345
## 46.28571 -0.3582288 -1.150886
## 46.42857 -0.3647824 -1.160909
## 46.57143 -0.3713076 -1.170889
## 46.71429 -0.3778047 -1.180825
## 46.85714 -0.3842741 -1.190719
## 47.00000 -0.3907161 -1.200572
## 47.14286 -0.3971311 -1.210382
## 47.28571 -0.4035195 -1.220153
## 47.42857 -0.4098815 -1.229882
## 47.57143 -0.4162174 -1.239572
## 47.71429 -0.4225277 -1.249223
## 47.85714 -0.4288126 -1.258835
## 48.00000 -0.4350723 -1.268409
## 
## $香川県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 2.393574 3.155577
## 46.28571 2.636503 3.429161
## 46.42857 2.643057 3.439184
## 46.57143 2.649582 3.449163
## 46.71429 2.656079 3.459099
## 46.85714 2.662548 3.468994
## 47.00000 2.668990 3.478846
## 47.14286 2.675405 3.488657
## 47.28571 2.681794 3.498427
## 47.42857 2.688156 3.508157
## 47.57143 2.694492 3.517847
## 47.71429 2.700802 3.527497
## 47.85714 2.707087 3.537109
## 48.00000 2.713347 3.546683
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 15.05434 16.60731 15.99877 16.52144 16.07253 16.45809 16.12694 16.41136
##  [9] 16.16707 16.37689 16.19668 16.35146 16.21852 16.33270
## 
## $愛媛県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 12.84306 11.672478
## 46.28571 14.01748 12.646510
## 46.42857 13.07600 11.528784
## 46.57143 13.41793 11.775034
## 46.71429 12.70354 10.920099
## 46.85714 12.91982 11.046764
## 47.00000 12.36477 10.373198
## 47.14286 12.48910 10.412782
## 47.28571 12.04905  9.869100
## 47.42857 12.10673  9.846242
## 47.57143 11.75097  9.397552
## 47.71429 11.76072  9.330521
## 47.85714 11.46741  8.952330
## 48.00000 11.44299  8.854528
## 
## $愛媛県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 17.26562 18.43620
## 46.28571 19.19714 20.56811
## 46.42857 18.92154 20.46877
## 46.57143 19.62495 21.26785
## 46.71429 19.44152 21.22496
## 46.85714 19.99637 21.86942
## 47.00000 19.88910 21.88067
## 47.14286 20.33363 22.40995
## 47.28571 20.28509 22.46504
## 47.42857 20.64705 22.90754
## 47.57143 20.64239 22.99581
## 47.71429 20.94221 23.37240
## 47.85714 20.96963 23.48471
## 48.00000 21.22242 23.81088
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 0.6060429 0.5951438 0.5850854 0.5758031 0.5672369 0.5593315 0.5520360
##  [8] 0.5453034 0.5390902 0.5333563 0.5280648 0.5231815 0.5186750 0.5145161
## 
## $高知県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 -1.072438 -1.960972
## 46.28571 -1.104694 -2.004534
## 46.42857 -1.132731 -2.042089
## 46.57143 -1.157179 -2.074564
## 46.71429 -1.178557 -2.102724
## 46.85714 -1.197299 -2.127203
## 47.00000 -1.213772 -2.148534
## 47.14286 -1.228283 -2.167163
## 47.28571 -1.241094 -2.183466
## 47.42857 -1.252427 -2.197764
## 47.57143 -1.262474 -2.210328
## 47.71429 -1.271397 -2.221390
## 47.85714 -1.279337 -2.231147
## 48.00000 -1.286415 -2.239770
## 
## $高知県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 2.284524 3.173058
## 46.28571 2.294981 3.194821
## 46.42857 2.302902 3.212260
## 46.57143 2.308785 3.226170
## 46.71429 2.313030 3.237197
## 46.85714 2.315962 3.245866
## 47.00000 2.317844 3.252606
## 47.14286 2.318890 3.257770
## 47.28571 2.319274 3.261647
## 47.42857 2.319140 3.264477
## 47.57143 2.318604 3.266458
## 47.71429 2.317760 3.267753
## 47.85714 2.316687 3.268497
## 48.00000 2.315447 3.268803
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 31.09824 34.36914 30.67560 24.69546 17.20267 17.87575 25.69330 30.47401
##  [9] 33.46178 31.29833 26.05699 20.67642 20.82228 25.51149
## 
## $福岡県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%        95%
## 46.14286 17.601263  10.456393
## 46.28571 18.452675  10.027006
## 46.42857 13.460137   4.346819
## 46.57143  6.795330  -2.680427
## 46.71429 -1.273000 -11.053430
## 46.85714 -1.445992 -11.674306
## 47.00000  5.057078  -5.867077
## 47.14286  7.264017  -5.022609
## 47.28571  8.400169  -4.866649
## 47.42857  5.006113  -8.912144
## 47.57143 -1.021542 -15.356051
## 47.71429 -7.068418 -21.755649
## 47.85714 -7.750867 -22.876576
## 48.00000 -4.204934 -19.935860
## 
## $福岡県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 44.59523 51.74010
## 46.28571 50.28561 58.71128
## 46.42857 47.89107 57.00439
## 46.57143 42.59559 52.07135
## 46.71429 35.67834 45.45877
## 46.85714 37.19750 47.42581
## 47.00000 46.32952 57.25367
## 47.14286 53.68399 65.97062
## 47.28571 58.52340 71.79022
## 47.42857 57.59054 71.50879
## 47.57143 53.13552 67.47003
## 47.71429 48.42126 63.10849
## 47.85714 49.39542 64.52113
## 48.00000 55.22792 70.95884
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 1.731702 1.731702 1.731702 1.731702 1.731702 1.731702 1.731702 1.731702
##  [9] 1.731702 1.731702 1.731702 1.731702 1.731702 1.731702
## 
## $佐賀県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%       95%
## 46.14286 -0.3016617 -1.378059
## 46.28571 -0.3727120 -1.486721
## 46.42857 -0.4414406 -1.591833
## 46.57143 -0.5080612 -1.693720
## 46.71429 -0.5727566 -1.792663
## 46.85714 -0.6356847 -1.888903
## 47.00000 -0.6969828 -1.982651
## 47.14286 -0.7567715 -2.074090
## 47.28571 -0.8151570 -2.163383
## 47.42857 -0.8722337 -2.250674
## 47.57143 -0.9280859 -2.336092
## 47.71429 -0.9827891 -2.419754
## 47.85714 -1.0364115 -2.501762
## 48.00000 -1.0890147 -2.582212
## 
## $佐賀県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 3.765065 4.841463
## 46.28571 3.836116 4.950125
## 46.42857 3.904844 5.055236
## 46.57143 3.971465 5.157124
## 46.71429 4.036160 5.256067
## 46.85714 4.099088 5.352307
## 47.00000 4.160386 5.446054
## 47.14286 4.220175 5.537493
## 47.28571 4.278561 5.626786
## 47.42857 4.335637 5.714077
## 47.57143 4.391489 5.799496
## 47.71429 4.446193 5.883157
## 47.85714 4.499815 5.965166
## 48.00000 4.552418 6.045615
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 0.9578604 1.3498231 1.3789750 1.0931350 1.0000008 1.0931352 1.0931357
##  [8] 1.0813964 1.2910766 1.3558034 1.0688301 1.0931422 1.0688305 1.0688309
## 
## $長崎県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%       95%
## 46.14286 -1.1494979 -2.265066
## 46.28571 -0.9965356 -2.238623
## 46.42857 -1.4501877 -2.947856
## 46.57143 -2.0206231 -3.668947
## 46.71429 -2.4321014 -4.248946
## 46.85714 -2.6024977 -4.558848
## 47.00000 -2.8615601 -4.955050
## 47.14286 -3.2581323 -5.555340
## 47.28571 -3.3316877 -5.778831
## 47.42857 -3.5668569 -6.172756
## 47.57143 -4.1203230 -6.867295
## 47.71429 -4.3572803 -7.242560
## 47.85714 -4.6270728 -7.642302
## 48.00000 -4.8642735 -8.005069
## 
## $長崎県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%       95%
## 46.14286 3.065219  4.180787
## 46.28571 3.696182  4.938269
## 46.42857 4.208138  5.705806
## 46.57143 4.206893  5.855217
## 46.71429 4.432103  6.248948
## 46.85714 4.788768  6.745118
## 47.00000 5.047832  7.141321
## 47.14286 5.420925  7.718133
## 47.28571 5.913841  8.360985
## 47.42857 6.278464  8.884363
## 47.57143 6.257983  9.004955
## 47.71429 6.543565  9.428844
## 47.85714 6.764734  9.779963
## 48.00000 7.001935 10.142731
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 3.231748 3.298643 3.091153 3.253990 3.402724 3.538575 3.662659 3.775996
##  [9] 3.879517 3.974071 4.060435 4.139319 4.211371 4.277182
## 
## $熊本県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286 -1.066917 -3.342492
## 46.28571 -2.250256 -5.187666
## 46.42857 -2.563366 -5.556687
## 46.57143 -2.692545 -5.840451
## 46.71429 -2.785398 -6.061192
## 46.85714 -2.851778 -6.234627
## 47.00000 -2.898604 -6.371926
## 47.14286 -2.930880 -6.481285
## 47.28571 -2.952303 -6.568850
## 47.42857 -2.965647 -6.639313
## 47.57143 -2.973015 -6.696298
## 47.71429 -2.976010 -6.742639
## 47.85714 -2.975867 -6.780562
## 48.00000 -2.973533 -6.811830
## 
## $熊本県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286  7.530412  9.805987
## 46.28571  8.847542 11.784952
## 46.42857  8.745671 11.738992
## 46.57143  9.200525 12.348431
## 46.71429  9.590846 12.866640
## 46.85714  9.928928 13.311776
## 47.00000 10.223922 13.697245
## 47.14286 10.482872 14.033278
## 47.28571 10.711337 14.327884
## 47.42857 10.913789 14.587454
## 47.57143 11.093885 14.817169
## 47.71429 11.254649 15.021278
## 47.85714 11.398610 15.203304
## 48.00000 11.527897 15.366194
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 6.671257 8.801423 7.345145 6.306988 7.036781 7.036781 7.036781 7.036781
##  [9] 7.036781 7.036781 7.036781 7.036781 7.036781 7.036781
## 
## $大分県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 4.928144 4.005395
## 46.28571 6.844878 5.809146
## 46.42857 5.076933 3.876214
## 46.57143 3.925164 2.664302
## 46.71429 4.642780 3.375473
## 46.85714 4.573714 3.269846
## 47.00000 4.506533 3.167101
## 47.14286 4.441090 3.067014
## 47.28571 4.377257 2.969390
## 47.42857 4.314920 2.874055
## 47.57143 4.253980 2.780854
## 47.71429 4.194346 2.689651
## 47.85714 4.135937 2.600323
## 48.00000 4.078681 2.512758
## 
## $大分県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                80%       95%
## 46.14286  8.414371  9.337119
## 46.28571 10.757969 11.793701
## 46.42857  9.613356 10.814075
## 46.57143  8.688813  9.949674
## 46.71429  9.430781 10.698089
## 46.85714  9.499847 10.803716
## 47.00000  9.567028 10.906461
## 47.14286  9.632471 11.006547
## 47.28571  9.696305 11.104171
## 47.42857  9.758641 11.199507
## 47.57143  9.819582 11.292707
## 47.71429  9.879216 11.383910
## 47.85714  9.937625 11.473238
## 48.00000  9.994880 11.560803
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 7.547759 7.978980 7.978980 7.978980 7.978980 7.978980 7.978980 7.978980
##  [9] 7.978980 7.978980 7.978980 7.978980 7.978980 7.978980
## 
## $宮崎県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%        95%
## 46.14286 4.644790  3.1080508
## 46.28571 4.918596  3.2985263
## 46.42857 4.555786  2.7436561
## 46.57143 4.227905  2.2422051
## 46.71429 3.926465  1.7811936
## 46.85714 3.645946  1.3521764
## 47.00000 3.382515  0.9492932
## 47.14286 3.133384  0.5682806
## 47.28571 2.896450  0.2059217
## 47.42857 2.670080 -0.1402814
## 47.57143 2.452976 -0.4723143
## 47.71429 2.244084 -0.7917866
## 47.85714 2.042538 -1.1000241
## 48.00000 1.847614 -1.3981350
## 
## $宮崎県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 10.45073 11.98747
## 46.28571 11.03936 12.65943
## 46.42857 11.40217 13.21430
## 46.57143 11.73006 13.71576
## 46.71429 12.03150 14.17677
## 46.85714 12.31201 14.60578
## 47.00000 12.57545 15.00867
## 47.14286 12.82458 15.38968
## 47.28571 13.06151 15.75204
## 47.42857 13.28788 16.09824
## 47.57143 13.50498 16.43028
## 47.71429 13.71388 16.74975
## 47.85714 13.91542 17.05798
## 48.00000 14.11035 17.35610
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 4.913713 4.381116 4.679739 4.234625 4.234625 4.234625 4.234625 4.234625
##  [9] 4.234625 4.234625 4.234625 4.234625 4.234625 4.234625
## 
## $鹿児島県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%       95%
## 46.14286  0.8278985 -1.335001
## 46.28571 -0.4303017 -2.977312
## 46.42857 -0.3505647 -3.013447
## 46.57143 -0.8934450 -3.608081
## 46.71429 -0.9111170 -3.635108
## 46.85714 -0.9287286 -3.662043
## 47.00000 -0.9462803 -3.688886
## 47.14286 -0.9637727 -3.715638
## 47.28571 -0.9812064 -3.742301
## 47.42857 -0.9985821 -3.768875
## 47.57143 -1.0159003 -3.795360
## 47.71429 -1.0331615 -3.821759
## 47.85714 -1.0503663 -3.848072
## 48.00000 -1.0675154 -3.874299
## 
## $鹿児島県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 8.999528 11.16243
## 46.28571 9.192533 11.73954
## 46.42857 9.710043 12.37292
## 46.57143 9.362695 12.07733
## 46.71429 9.380367 12.10436
## 46.85714 9.397979 12.13129
## 47.00000 9.415530 12.15814
## 47.14286 9.433023 12.18489
## 47.28571 9.450457 12.21155
## 47.42857 9.467832 12.23812
## 47.57143 9.485150 12.26461
## 47.71429 9.502412 12.29101
## 47.85714 9.519617 12.31732
## 48.00000 9.536766 12.34355
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##  [1] 29.49374 24.98064 26.73174 23.40514 20.66010 20.87795 22.63689 22.36678
##  [9] 22.36678 22.36678 22.36678 22.36678 22.36678 22.36678
## 
## $沖縄県$lower
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##                 80%         95%
## 46.14286 17.5257513  11.1902814
## 46.28571 11.9324765   5.0251938
## 46.42857 12.6862212   5.2509721
## 46.57143  8.4285424   0.5004087
## 46.71429  4.8070079  -3.5851122
## 46.85714  4.1943516  -4.6374123
## 47.00000  5.1622106  -4.0883261
## 47.14286  3.1840109  -6.9707351
## 47.28571  2.1517004  -8.5495178
## 47.42857  1.1696043 -10.0515042
## 47.57143  0.2310380 -11.4869174
## 47.71429 -0.6693197 -12.8638957
## 47.85714 -1.5357869 -14.1890429
## 48.00000 -2.3719249 -15.4678056
## 
## $沖縄県$upper
## Time Series:
## Start = c(46, 2) 
## End = c(48, 1) 
## Frequency = 7 
##               80%      95%
## 46.14286 41.46173 47.79720
## 46.28571 38.02881 44.93609
## 46.42857 40.77726 48.21251
## 46.57143 38.38174 46.30988
## 46.71429 36.51319 44.90531
## 46.85714 37.56155 46.39331
## 47.00000 40.11157 49.36211
## 47.14286 41.54955 51.70430
## 47.28571 42.58187 53.28308
## 47.42857 43.56396 54.78507
## 47.57143 44.50253 56.22048
## 47.71429 45.40289 57.59746
## 47.85714 46.26935 58.92261
## 48.00000 47.10549 60.20137